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Reviewing North American Gas and Oil Field Distribution

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Justin Napolitano

2022-05-05 18:40:32.169 +0000 UTC


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North American Gas and Oil Fields

North American gas and oil fields contain spent wells that can be used to store carbon and green hydrogen. They may also potentially yield more resources with the injection of super critical co2 into the wells.

An analysis of the capcity of wells requires an overview of the fields in general. In this post, I identify the gas fields to later create a model that will predict the price of storing carbon and possibly hyrdogen in spent wells.

Data Import

import pandas as pd
import matplotlib.pyplot as plt
import geopandas as gpd
import folium
import contextily as cx
import rtree
from zlib import crc32
import hashlib
from shapely.geometry import Point, LineString, Polygon
/Users/jnapolitano/venvs/finance/lib/python3.9/site-packages/geopandas/_compat.py:111: UserWarning: The Shapely GEOS version (3.10.2-CAPI-1.16.0) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.
  warnings.warn(

Oil and Natural Gas Field Data

## Importing our DataFrames

gisfilepath = "/Users/jnapolitano/Projects/data/energy/Oil_and_Natural_Gas_Fields.geojson"

fields_df = gpd.read_file(gisfilepath)
na = fields_df.PR_OIL.min()
fields_df.replace(na, 0 , inplace=True)


fields_df = fields_df.to_crs(epsg=3857)

fields_df.describe()

OBJECTID PR_OIL PR_GAS SHAPE_Length SHAPE_Area
count 224.000000 224.000000 224.000000 224.000000 224.000000
mean 112.500000 1530.496585 87.685987 16.473665 10.386605
std 64.807407 17219.764834 644.145040 43.284473 45.170003
min 1.000000 0.000000 0.000000 0.100238 0.000594
25% 56.750000 0.000000 0.000000 2.511389 0.221885
50% 112.500000 0.000000 0.000000 6.563044 1.225873
75% 168.250000 0.000000 0.000000 14.317886 5.378536
max 224.000000 238050.000000 8446.000000 485.692251 448.052251

.. index::
   single: Oil/Gas Fields Map by Commodity

Oil Gas Field Map by Commodity

fields_map =fields_df.explore(
    column="COMMODITY", # make choropleth based on "PORT_NAME" column
     popup=False, # show all values in popup (on click)
     tiles="Stamen Terrain", # use "CartoDB positron" tiles
     cmap='Reds', # use "Set1" matplotlib colormap
     #style_kwds=dict(color="black"),
     marker_kwds= dict(radius=6),
     tooltip=['NAICS_DESC','REGION', 'COMMODITY' ],
     legend =True, # use black outline)
     categorical=True,
    )


fields_map
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